This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Scientists have identified the conditions that make it possible to predict some El Niño and La Niña events up to a year in advance, potentially giving communities and decision-makers significantly more time to prepare for weather extremes. The research published today in Science Advances has found that the state of the Pacific Ocean at the beginning of a forecast plays a critical, and often overlooked, role in determining whether long-range predictions of the El Niño–Southern Oscillation (ENSO) will be accurate.
ENSO events, which include El Niño and La Niña phases, influence weather patterns around the globe. While these events typically peak around December and can usually be forecast with confidence around six months in advance, predicting them earlier has long been a challenge. Australian scientists at CSIRO and the University of Melbourne analyzed more than 7,500 historical ENSO forecasts from climate models developed in nine countries and found that knowing whether the Pacific is in an El Niño, La Niña or neutral state when a forecast is started can provide valuable clues about how reliable a forecast is likely to be one year later.
The study found that forecasts initiated during La Niña conditions consistently performed well even when predicting ENSO events a year ahead, while forecasts beginning from neutral conditions were far less reliable. Mandy Freund, from the University of Melbourne and a researcher with the Australian Research Council Centre of Excellence for 21st Century Weather, said the findings provide a new way to assess confidence in long-range climate forecasts. "Our study identifies valuable additional information for predicting El Niño and La Niña events well ahead of time," Freund said.
"The finding that one-year forecasts starting from La Niña perform well in all nine of the models tested suggests there is something about La Niña conditions that makes the tropical Pacific system more predictable over the following year." Lead author Dr. Nandini Ramesh, senior research scientist at CSIRO and adjunct research fellow at UNSW Sydney, said the research could help improve how early warnings are communicated. "We've known for some time that the starting conditions affect how well model forecasts perform, but our study really shows how drastic those differences in performance can be," Ramesh said.
"The exciting thing about our results is that we can use that information. We can identify which forecasts deserve greater confidence and potentially warn the public about El Niño events much earlier." The researchers also found that different forecasting approaches may be suited to different climate conditions. While traditional physics-based models struggled when forecasts began from neutral conditions, machine learning and statistical models may have potential to improve performance in these situations.
The findings highlight opportunities to strengthen early-warning systems, although further research is needed before the approach can be incorporated into operational forecasting systems. "We are still a few steps away from applying our findings to the models used in practice by forecasting agencies," Ramesh said, "but these results hold promise for being able to communicate early warnings for future events." The research offers new insights into the drivers of ENSO predictability and could ultimately help improve preparedness for droughts, floods and other climate impacts associated with El Niño and La Niña events. Nandini Ramesh, Using initial states to identify successful El Niño–Southern Oscillation forecasts 1 year in advance, Science Advances (2026).
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